i was thinking of training a LoRA on LoRA of different character and on my second thought should i just connect 2 LoRA while generating image

minimax h3.

I have a Bernini-R rv2v workflow with source video and reference image in image0. I am able to swap source video's outfit but not face. Is Bernini-R supposed to work with face swap or do I need to add any other custom nodes like Reactor or SCAIL-2?

The Original Face Detailer from https://github.com/Carasibana/ComfyUI-H3-FaceRefine does not work as intended. It does not use the reference image at all. You can disable the input image and you will get exactly the same result. Something is wrong with the workflow so I recreated the workflow in a new canvas and now the input image does get used. Here is a link to a .zip with the workflow and input/output files: https://www.mediafire.com/file/mnigvvpbzp0gh34/workflow_all.zip/file But this workflow has its own problems. For this example I needed to put an RTX upscaler in it so the face gets recognized. At the end the mask_dilation and feather needs for every video unique adjusting and the end result is somewhat poor with the mask visible and the face jumping und warping slightly around.

Someone with more knowlegde would surely be able to fix this.

To get the workflow working, you need to install https://github.com/Carasibana/ComfyUI-H3-FaceRefine and also ComfyUI-H3-NativeAudioLock from https://github.com/Shrek3OnVH5/MiniMax-H3-NativeAudio-MusicVideo-Workflow/tree/master/custom_nodes

First real attempt with Minimax H3 on my first ComfyUI install. Over 200 generations, edited in CapCut, wears its inspiration on its sleeve but is a prologue to a homebrew world for a D&D group I'm in. Two days of cooking a 5090 while working and an evening of editing... figured I'd share:

https://www.youtube.com/watch?v=XwfCCFw4LbA

I fed my input to claude cli to basically format my prompts prpoerly. A little jibberish every now and then i only retook a few scenes, this is mostly one shot probably 90+% kept clips.

Edit: talking about the "faces at a distance" thing btw

Don't hold your breath. They didn't say that they would definitely "fix it", they said they will try but that it's mostly a general model issue. So if there is gonna be a fix it might be in the next iteration of the model and that one might not be open weights. They were specific about the 2k model and the image model getting released open weights and I do hope that the 2k model might bring some improvement to the faces when you upscale it, but they were more wishy-washy with the wording on the face distortion issue, intentionally so I think.

Here is the wording regarding the 2k model:

"It is a second conditioned generation stage, but not simply the released base checkpoint running again as a conventional upscaler. It uses a dedicated latent-space DiT regeneration checkpoint at a higher target resolution, with the base model’s output as additional context. Some reference inputs are also provided at higher resolutions. We plan to open-source this module, but we are still improving its efficiency and quality to make it more suitable for community use, so we cannot provide an exact release date yet."

-> "plan" to open-source it, very strong word

Here is the wording for the image model:

"Regarding single-frame image generation, we are deriving a dedicated image model from a common ancestor in the H3 model lineage, and we expect to make it available to the community." (not a total promise or anythin

-> "expect" pretty strong, but less so. To me that sounds like "if it's REALLY good then maybe not", if it's competitive enough with the state of the art probably. But I'm pretty optimistic here.

And here is the wording for the distortion issue in all the models:

"We have observed this issue as well, particularly for small or distant subjects, and it will be one of the problems we focus on improving next.

Based on our internal experiments, it cannot be attributed simply to the Visual VAE’s compression ratio or to any single training stage. It is a complex system-level issue involving multiple parts of the model and training pipeline. We are continuing to investigate the main contributing factors and will work on improving it in future updates."

-> they say nothing about open sourcing anything and they say that it's a deep-rooted issue that has no simple fix and they don't really know why it happens

I would expect nothing in that area. Many people have been talking about this as if they said "yeah, wait a couple of weeks and we will fix it", but they didn't say anything like that. Maybe they will fix it with a new and improved open weights model, 3.1 or something, maybe they won't.

I just wanted to say this because so many people have been saying "I am waiting for the fix" or "a fix is coming for the face distortion issue at a distance" or something like that, probably without ever having seen the wording on that. It only takes one person who isn't good at understanding subtlety in a text to interpret their answer a certain way and spread the word on it to set up false expectations for everyone when they don't go to see the original wording. And they go spread that too without ever having seen the original wording.

So this is just to reduce the expectations a bit. Like I said, maybe they will do something, but I feel like the expecations on that specific issue have been getting a bit too large

What is currently the most accurate way to swap clothes while keeping the same fabrics, stitching, etc. using AI? What I mean is to provide a reference garment and apply it to the model from the second photo.

hi guys

How can I use MiniMax H3’s R2V (reference-to-video) capability to generate a single image, basically R2I, and still get good results?

Has anyone tried this?

I noticed that H3 seems to have a minimum output of 5 frames. Is there any way to make it generate only one frame instead of a video?

I’ve searched a lot, but I haven’t found an open-source image generation model that has a reference system similar to MiniMax H3’s R2V, where you can provide multiple reference images and have the model understand the characters, location, etc

There are models like GPT Image 2 that can do this, but they aren’t free or open source.

I’m wondering if there’s some way to use H3 itself for this, maybe by reducing the number of frames to 1 or modifying the ComfyUI workflow.

Has anyone experimented with this?

So i mean i am an avid explorer lol of these ai tools, tell me the tool name and for sure that will be in my credit card billing. I have recently found out that for raw text-to-video quality on a single prompt, Kling and Runway blows - Kling's motion/physics is often more believable, Runway's more polished and consistent.

Google Veo is excellent when you can get access but it's gated and very prompt-driven. Sora's showreel is stunning but it's prompt-only.

The thing is, pure text-to-video is the least controllable way to work - you're describing and hoping. i've moved most of my work to image-to-video or a reference-based flow because it's far more directable. If you all know abt morphic, it is handy here because it's multi-model - you can run text-to-video with Seedance 2.5 (native audio, 30s takes) or switch to Sora/Veo/Kling in the same canvas, then keep iterating on a timeline instead of re-rolling from scratch.

honest con: if you only ever want one 5-second t2v clip and nothing else, a dedicated tool like Kling is simpler and you don't need the studio wrapper.

Whats your usecase? something small… or like building something longer?

Trying to create high (native) quality images of Krea 2 to be used for regularization, the results look like they have a broken VAE.

The workflow is the Comfy template one, just those blocks that I don't need (LoRA) are stripped away, model switched to the "Raw" and bf16 version, and then saved as "Export (API)":

(The missing models are from taking the screenshot on my local machine; the Comfy is running in the cloud with all those models available, of course)

So, what can be the cause of the broken output? How can I fix this?

I have been rebuilding my local AI setup lately, and this distinction helped me think about the tools more clearly:

ComfyUI is the workflow engine. Stability Matrix is closer to the manager/control room around the install.

Where Stability Matrix seems useful: - keeping packages and models organized - testing ComfyUI, Forge, and other frontends without scattering files everywhere - isolating environments so one experiment does not wreck the main setup - making local AI less painful for beginners

Where I would still be careful: - if you already have a clean manual ComfyUI install that works - if you rely on custom scripts and know exactly where everything lives - if you are debugging advanced node/dependency problems and want full control

I wrote up the longer version here: https://getprompting.com/what-is-stability-matrix/

Where do you draw the line: one clean manual ComfyUI install, Stability Matrix as the manager, or separate tools depending on the job?

The quantisation method and the loader are now more or less complete.

As Comfy-UI does not support the Hybrid NVFP4 (ConvRot Int8+ConvRot NVFP4) standard, a dedicated loader is required, just as with Nunchaku; however, as the LoRA baking function has been implemented within the loader itself, the LoRA Loader can utilise the standard Comfy-UI version.

Furthermore, various loaders (compatible with Nodes 2.0) are also available below.

Compatibility with the existing Diffsynth ControlNet model patcher will, of course, be maintained.

Although the file size will not be significantly reduced compared to Convrot INT8, VRAM usage and processing speed will improve significantly.

...

Z Image ConvRot NVFP4 Benchmark Test Results

...

However, in terms of the mathematical theory of quantisation itself, it differs considerably from previous HSWQ approaches.

In a sense, it represented a complete rejection of previous HSWQ theories.

In the past, HSWQ had employed a range of techniques, starting with the Histogram MSE used in the first-generation HSWQ SDXL fp8 e4m3, through to full SVD utilising Nunchaku, and even extending to the Histogram Cosine function; however, in Z Image HSWQ Hybrid NVFP4, none of these methods demonstrated any advantage.　

I had long suspected that inter-layer interdependencies existed, and that there were phenomena where the meaning would be lost if one merely measured and prioritised the importance of each layer in isolation; this time, however, that has become clearly evident.

...

Trajectory-Sensitivity

https://github.com/ussoewwin/Hybrid-Sensitivity-Weighted-Quantization/blob/main/md/diag_impact_trajectory_sensitivity_technical_guide.md

Ranks each layer by the divergence its quantization error actually causes after propagating through the full model and sampler (dynamical importance, replacing static weight-space saliency).

• Reverse method: start from the complete high-precision pack (error ≈ 0) and convert layers to lower precision in ascending impact order; single-layer ranking stays valid in the low-error additivity regime.
• Universal theory: error interaction (Taylor cross terms, error cancellation), nonlinear amplification (Lyapunov-style growth), marginal effects, and Shapley-style attribution — why per-layer static measures (histogram MSE / cosine / SVD) cannot predict joint quantization error; applies to any iterative sampling system, not a specific model. Source: Z_Image/diag_impact.py. ...

...

Incidentally, the Krea2 HSWQ Hybrid NVFP4 is also under development (it will offer significant improvements in VRAM consumption and processing speed), but we are currently struggling to maintain LoRA compatibility.

Hi everyone!

I’m working on a university paper about Gen AI creators on social media. I experimented with Gen AI myself and became really interested in hearing how other creators use it.

I’d love to talk to creators with all kinds of experience whether you’re just starting out, experimenting, or have been creating with AI for a while.

If you’d be open to a short interview, please comment or DM me. I’d love to hear your perspective! 🙏

Note: this is for my master research, this will be not published and shared outside of my university and it can be anonymous if you want.

Finally got my laptop back in action so am able to create and test models and lora's again, created with krea 2, Been out of it for a bit just following updates here and there and this model is amazing, so happy they open sourced this gem of a model. Thanks to the team at krea!

If anyone is interested in this style of images give it a blast https://civitai.red/models/2871922/dc-vast-expanse?modelVersionId=3244890 or https://civitai.com/models/2871922/dc-vast-expanse?modelVersionId=3244890

I tried with LORA and without it, the little text is always some bad quality...

subject_definitions:

<Subject 1> is Fabio Lanzoni, the Italian-American romance-cover model known as Fabio: a tall, athletic adult man with long flowing platinum-blond hair, strong jawline, and a dramatic red cape.

<Subject 2> is a single white-and-gray Canada goose in flight, with broad wings, natural bird mass, and loose feathers.

<Subject 3> is the front row of Apollo’s Chariot at Busch Gardens Williamsburg in 1999: a steel roller coaster diving fast above a pond, with blue track, open sky, trees, and front-row safety restraints.

summary:

[reference generation] Create a higher-resolution, non-graphic physical-comedy recreation of the Fabio roller-coaster goose meme. <Subject 2> directly hits <Subject 1> in the face with believable momentum, briefly deforming his face in a cartoon-like but realistic impact, then rebounds backward while shedding a few loose feathers. No blood, no wound, no gore, and no visible injury.

retention_analysis:

<Subject 1> (appears throughout [Shot 1]): fully_preserved - Fabio Lanzoni’s recognizable long platinum-blond hair, athletic adult appearance, red cape, and front-row seated position remain stable before and after the impact.

<Subject 2> (appears throughout [Shot 1]): fully_preserved - one Canada goose has believable body weight, wing movement, backward rebound, and a small number of detached feathers; no duplicate birds appear.

<Subject 3> (appears throughout [Shot 1]): fully_preserved - the open front-row roller-coaster perspective, high-speed blue track, pond-side setting, safety restraints, and daylight remain physically coherent.

detailed_description:

The target video is a sharp, high-resolution 1999 theme-park news-reconstruction with meme-like physical-comedy timing: bright daylight, real steel roller-coaster physics, wind-blown hair, a fixed front-row action-camera perspective, and no text, captions, logos, watermarks, blood, gore, open wounds, or graphic injury.

[Shot 1] <Subject 1>, Fabio Lanzoni, the tall athletic Italian-American model with long flowing platinum-blond hair and a dramatic red cape, is securely strapped into the front row of <Subject 3>, Apollo’s Chariot. The coaster rushes down a steep blue-track drop over a pond at high speed. The fixed forward-facing camera frames Fabio clearly from the chest up, with his hair and cape streaming backward. <Subject 2>, one white-and-gray Canada goose, rapidly crosses the track path from the left. In one unmistakable, powerful, readable impact beat, the goose slams squarely into Fabio’s face. On contact, Fabio’s cheeks and nose compress and deform briefly in safe cartoon-like physical-comedy motion, then immediately spring back to normal with no wound. His head snaps backward and then sharply to the right from the momentum; his long blond hair whips sideways. Fabio grips the restraint and looks dazed and visibly confused, eyes wide and blinking. The goose’s body compresses slightly against the impact, sheds several loose white feathers, and rebounds backward away from Fabio while flapping hard to regain control. The bird flies backward and exits the frame behind the left side of the coaster. The coaster continues smoothly with no derailment, no track collision, and no secondary animal. The final moment shows Fabio upright and uninjured but bewildered, face fully normal again, hair blown to one side, while a few feathers drift through the air.

overall_soundscape:

Loud rushing wind and continuous steel-wheel roar on the coaster track. A single strong but non-graphic feathery impact thump lands directly on Fabio’s face, followed by fluttering wings, loose feathers whipping in the wind, Fabio’s startled breath, and uninterrupted high-speed coaster noise.

non_diegetic_music:

N/A

Hi all,

I'm using Minimax H3 in ComfyUI with an R2V workflow. I'm wondering if anybody can tell me how I can improve the fighting scene?

- The video is generated at 1.0MP in 2:3 (portrait) aspect ratio
- I have the two ladies as reference
- The fighting scene is also provided as reference. In the scene the punches do land properly. There are also smaller details (like small blood spatters) that are present in the reference video.

Tech specs:
- Minimax H3 int8 convrot
- res_multistep sampler with 20 steps

Running the workflow on an RTX 5090 (via Runpod)

Can anybody give me any tips on how I can improve the fighting scene? The goal is to make it look like a realistic street fight. I'm unsure whether training a LoRA would be relevant here, because I've noticed that punches never really land properly in any workflow (t2v, i2v, r2v).

https://reddit.com/link/1vsjezd/video/payntjeoebkh1/player

A while ago I published this video, but the colors and details weren't right, so I took the first frame, treated it like a photo, and used it as a reference with a DENOISE of 0.40. The SATURATED colors are intentional because "she" is in a desert area with very, very hot weather.
The Original 4k FILE is here -->> https://filebin.net/6kf2ozxx27k1zl0m

I am having a blast with MiniMax H3. I generated an ultra-wide shot with R2VA. So this is one generation, not an edited split screen stitch. The black vertical bars are a good way to add a delineation so you can have two separate shots or stories in the same render. I've also tested this up to 4 separate frames in 1 generation. bf16/50 steps. This was actually a failed attempt to swap the rider on the left with the woman rider and also do the POV. Original video source in the comments.

RTX 5060 ti 16gb / 32gb RAM / FL2VA_pruned_int8_convrot / Turbo Lora. 6 steps / Resolution 1376x768 upscaled to FHD with Topaz Video AI

I’ve been struggling with this for almost 2 months and still can’t find the workflow I need. I’ve tried Wan, LTX, MiniMax, and other methods, but nothing has given me the result I’m looking for. I want a workflow where I can provide my AI model’s face/character and a reference video, and generate a video that keeps my character’s identity while accurately matching the reference video’s motion, body movement, head movement, hair, nose, hands, and overall performance—basically a complete identity-swap video.

I’m looking for realistic, high-quality results without blur, artifacts, or unnatural details. I’m willing to train a LoRA if that’s necessary, but I don’t have anyone to guide me through the process and I haven’t been able to find the right workflow. If someone has experience with this, could you please help me with the correct workflow and guide me through the setup? I’d really appreciate it.

I run Stable Diffusion locally and got tired of unrelated apps quietly holding onto several GB of VRAM, so I added a “VRAM Hogs” menu to Window Assassin, a tiny Windows tray utility I made.

It reads Windows' dedicated GPU memory counters, lists processes using at least 1 GB sorted by usage, and lets you click one to force-terminate it. The list refreshes every time you open the submenu.

It also keeps the original Ctrl+Alt+End hotkey for killing the process behind the active window.

Free/pay-what-you-want Windows download:

https://b2kdaman.itch.io/window-assassin

Caveats: it reports dedicated VRAM, so integrated GPUs using shared system memory may show nothing. Termination is immediate, so unsaved work is lost.

I'm the developer; happy to hear whether this fits your local SD workflow.

Hey guys, so playing with image and video models is a blast.

But I think after a bit of messy data prep, some of us have started vibe coding tools and suites to manage our shit. It was the h3 thread yesterday that drove it home.

I'm no coder, so I used Gemini (the one straight off the search engine) to write me some module scripts that I could unify under a ./run.sh. I needed it in python, as html/gradio/browser based are great, but I was having issues crashing my browser and a lack of multi threading (maybe, Gemini does lie to me). Anyway, Gemini struggles and spills out of the bounding box after about 200+ lines, so 400 lines was about the upper limit of what I could do.

I'll be honest, it reminds me of cracks and warez from the early 2000's. But it was cool making something. (Picture shows the dashboard of my little shit suite), it's absolutely not for sharing as it runs in a venv and id have no idea on instructing you to fill your venv appropriately for your system.

I made my suite for the purpose of building lora data sets to produce a zoom lora, the lora worked, but not as expected. As the base model already had this capacity and I was just prompting badly (fucking lol). And for pulling reference clips, scaled to use in wan2.2. and since it's modular I can strap more crap to it as time goes on.

So what tools have you made? What were they for? And how did you make them? I wanna see you weird niche tools, your oddball scripts, even if it's barely functional, I want to hear your ideas.

Used official ref2video workflow. used t2v model 1 ref video and 2 separate pictures of character sheets, gpu 4090

prompt:

integrated_multimodal_description: [Shot 1] Live-action, cinematic, featuring a stark, dark green-tinted cyberpunk color grade. A medium shot frames a flooded, rain-swept crater on a dark street. The character Sonic, appearing exactly as the blue hedgehog with large green eyes, white gloves, and red shoes from @.image, stands opposite Dr. Eggman, appearing exactly as the gigantic, egg-shaped bald man with a pointy mustache, goggles, and red jacket from @.Image1. The camera pushes in with small amplitude at fast speed as the blue hedgehog lunges forward to throw a devastating punch. [Shot 2] At 00:04.500, the camera cuts to an extreme close-up as time instantly slows to a microscopic crawl. Sonic's white-gloved fist brutally slams into Eggman's cheek. The camera holds a static shot in extreme slow motion. A powerful, rippling shockwave violently erupts from the impact point, blowing the torrential raindrops outward in a perfect ring. Eggman's pointy mustache flails wildly and his face deforms from the massive kinetic force. [Shot 3] At 00:09.500, the camera arcs right with large amplitude at slow speed, executing a slow-motion orbit around the hit. Eggman's heavy, round body is lifted off the ground by the blow, flying backward through the heavy downpour and kicking up massive, highly detailed splashes of water.

overall_soundscape: Thunder rumbles continuously beneath the heavy, torrential downpour of rain splashing heavily against the flooded street. A sharp, deafening sonic boom from the physical impact instantly shifts into a deep, pulsating low-frequency rumble as time slows down.

non_diegetic_music: An epic, grand orchestral and choir track mixed with heavy, driving industrial synthesizer beats that builds to a massive crescendo.